---
license: cc-by-sa-4.0
task_categories:
  - visual-question-answering
  - question-answering
language:
  - en
tags:
  - azure
  - cloud-architecture
  - solution-architect
  - architecture-diagrams
  - vision-language-model
  - fine-tuning
  - VQA
size_categories:
  - 1K<n<10K
---

# Azure Architecture Visual Question Answering Dataset

A comprehensive visual question answering (VQA) dataset for fine-tuning vision language models to become Azure Cloud Solution Architects. Created from the [Azure Architecture Center](https://learn.microsoft.com/en-us/azure/architecture/).

## Dataset Description

This dataset contains Q&A pairs paired with Azure architecture diagrams, designed for fine-tuning vision language models (like Qwen 3.5 VL) to understand and reason about cloud architecture patterns.

### Source
- **Azure Architecture Center**: 373 architecture pages scraped
- **Architecture Diagrams**: 500+ high-quality architecture diagrams (PNG format)
- **Q&A Generation**: Generated using Qwen 2.5 72B Instruct via HuggingFace Inference API

### Question Categories
Each architecture generates 5 Q&A pairs across these categories:

1. **Architecture Overview** (free_form): High-level design purpose and patterns
2. **Component Identification** (free_form): Azure services used and their roles
3. **Design Decision** (multi_choice): Design choices and best practices
4. **Visual Understanding** (free_form): Diagram layout, data flow, and connections
5. **Scenario Application** (multi_choice): When and how to apply the pattern

### Architecture Categories Covered
- Reference Architectures (containers, networking, identity, etc.)
- Example Scenarios (IoT, data, mainframe, SAP, etc.)
- Design Patterns (CQRS, Event Sourcing, Gateway, etc.)
- Solution Ideas (analytics, AI/ML, hybrid, etc.)
- Best Practices and Guides
- Cloud migration patterns
- Microservices architectures
- Serverless patterns
- Hybrid and multi-cloud designs

## Dataset Structure

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `pid` | string | Unique question ID |
| `question` | string | The question text |
| `image` | string | Image filename reference |
| `decoded_image` | Image | The architecture diagram (PIL Image) |
| `choices` | list[string] | Answer choices for multi_choice, empty for free_form |
| `answer` | string | The correct answer |
| `question_type` | string | "free_form" or "multi_choice" |
| `answer_type` | string | Answer format type |
| `metadata` | string | JSON with category, skills, source, page info |
| `query` | string | Formatted query with hints |

### Splits

| Split | Rows |
|-------|------|
| train | 1,678 |
| test | 187 |

## Intended Use

### Fine-tuning Vision Language Models
This dataset is specifically designed for fine-tuning models like:
- **Qwen 3.5 VL** (0.8B, 3B, 8B variants)
- Other vision-language models that support image+text input

### Target Capability
Train a model to act as an **Azure Cloud Solution Architect** that can:
- Analyze architecture diagrams
- Identify Azure services and their roles
- Explain design decisions and trade-offs
- Recommend architectures for given scenarios
- Understand data flow and system interactions

## Example

```python
from datasets import load_dataset

ds = load_dataset("thegovind/azure-architecture-vqa")

# View a sample
sample = ds['train'][0]
print(f"Question: {sample['question']}")
print(f"Answer: {sample['answer']}")
print(f"Type: {sample['question_type']}")
if sample['decoded_image']:
    sample['decoded_image'].show()
```

## Citation

```bibtex
@dataset{azure_architecture_vqa_2026,
  title={Azure Architecture Visual Question Answering Dataset},
  author={thegovind},
  year={2026},
  url={https://huggingface.co/datasets/thegovind/azure-architecture-vqa},
  note={Created from Azure Architecture Center documentation}
}
```

## License

This dataset is released under CC-BY-SA-4.0. The source content is from Microsoft's Azure Architecture Center documentation.